The answer is to impute missing values with the mean of the column or remove rows with missing values if the proportion is small. Imputation with the mean preserves the overall distribution of the feature, which is critical for linear regression because the model assumes a linear relationship and is sensitive to biased inputs. Removing rows is acceptable when the missing data is minimal and random, as it avoids distorting the coefficient estimates. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that linear regression cannot inherently handle missing values—unlike tree-based models—so you must preprocess the data. A common trap is selecting “set missing values to zero,” which artificially shifts the intercept and biases the model. Remember the mnemonic: “Mean or drop, never zero or stop” to recall that imputation with the mean or deletion of sparse missing rows are the appropriate techniques for linear regression on SageMaker.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which TWO techniques are appropriate for handling missing values in the dataset?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove rows with missing values if the proportion is small
Options A (Impute with the mean of the column) and C (Remove rows with missing values if the proportion is small) are correct. Imputation is a common technique; removing rows is acceptable if few missing. Option B (Set missing values to zero) can bias the model. Option D (Use a decision tree that handles missing values internally) is not applicable for linear regression. Option E (Use a separate category for missing) is not suitable for linear regression numeric features.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use a decision tree model that can handle missing values internally
Why it's wrong here
The question specifies linear regression, which cannot handle missing values natively.
✗
Set missing values to zero
Why it's wrong here
Setting to zero can introduce bias and is not recommended without reason.
✓
Remove rows with missing values if the proportion is small
Why this is correct
If a small fraction of rows have missing values, removing them is acceptable.
Related concept
Read the scenario before looking for a memorised answer.
✓
Impute missing values with the mean of the column
Why this is correct
Mean imputation is a simple and common technique for numeric features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a separate category for missing values
Why it's wrong here
This is more appropriate for categorical variables, not linear regression numeric features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove rows with missing values if the proportion is small — Options A (Impute with the mean of the column) and C (Remove rows with missing values if the proportion is small) are correct. Imputation is a common technique; removing rows is acceptable if few missing. Option B (Set missing values to zero) can bias the model. Option D (Use a decision tree that handles missing values internally) is not applicable for linear regression. Option E (Use a separate category for missing) is not suitable for linear regression numeric features.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?
easy
A.Ignore missing values; linear regression can handle them.
✓ B.Impute missing values with the mean of the column.
C.Replace missing values with zeros.
D.Remove all rows containing missing values.
Why B: Option B is correct because linear regression models in Amazon SageMaker cannot handle missing values natively; they require complete numerical input. Imputing missing values with the column mean is a standard preprocessing technique that preserves the overall distribution and avoids introducing bias, ensuring the SageMaker built-in Linear Learner algorithm can train without errors.
Variation 2. A data scientist is using Amazon SageMaker to train a linear regression model. The training job fails with the error: 'AlgorithmError: Input data has NaN values'. Which step should the data scientist take to resolve this issue?
medium
A.Convert the data to a sparse format
B.Switch to a different algorithm that handles missing values
✓ C.Impute missing values or remove rows with NaN values
D.Increase the number of training instances
Why C: The error indicates NaN values in the input data. The correct action is to handle missing values before training. Option A is wrong because increasing instance count does not fix data issues. Option C is wrong because the error is data-related, not algorithm-related. Option D is wrong because the issue is NaN values, not data format.
Last reviewed: Jun 20, 2026
Question Discussion
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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